Arnesh Banerjee

Advisor: Dr. Sourangshu Bhattacharya

• Built six independently trained per-modality models (bulk RNA-seq, DNA methylation, miRNA, copy-number, somatic-mutation, and a leakage-safe clinical model) by porting the Cedars-Sinai / Betteromics Molecular-Twin methodology to the TCGA head-and-neck cohort, evaluated over 1,206 samples from 1,160 patients (58,147 genes) under patient-grouped, label-stratified 5-fold cross-validation with zero leaked patients.
• Reframed the task from binary oral-vs-rest detection to ten-class tumour typing, reaching 0.84 accuracy and 0.90 balanced accuracy while confining almost all error to the clinically hard oral vs non-oral head-and-neck block that the binary framing had masked.
• Engineered a deployed decision-support system in which a self-hosted Qwen3-32B orchestrator aggregates the six model predictions weighted by cross-validated reliability, backed by a knowledge-graph RAG fallback on low confidence and a deterministic safety layer that only escalates risk.
• Diagnosed a training-set leakage confound (weak modalities scoring a perfect 1.000 against honest ROC-AUCs of 0.60 to 0.82) that capped both the LLM and ML baselines at a ~0.98 ceiling, and specified the held-out-cohort correction required for a valid comparison.

CMATER Lab · Advisor: Prof. Debotosh Bhattacharjee

• Designed a hybrid CNN-Transformer segmentation model (ResNet-34 encoder, ASPP, Transformer bottleneck, and SE-gated skip connections with a differentiable Chan-Vese level-set loss), reaching 0.9716 Dice and 0.9463 IoU on the DMR-IR dataset (357 thermograms, 119 patients) under patient-stratified 5-fold cross-validation.
• Surfaced an annotation-quality ceiling in weakly supervised thermography by benchmarking against four state-of-the-art baselines (Attention U-Net, UNet++, DeepLabV3+, TransUNet) across five metrics (Dice, IoU, HD95, ASSD, BF1) and showing all models converge to a statistically indistinguishable Dice of ~0.97 (p > 0.05, paired Wilcoxon with 1,000-resample bootstrap CIs).
• Built a robustness battery (label-noise injection at 10% to 30%, augmentation regimes, 25% to 100% training subsets) and an explainability suite (Grad-CAM, attention maps, Monte-Carlo dropout uncertainty) to make the model clinician-facing.

Learning-based Decision Making Lab · Advisor: Dr. Arnob Ghosh

• Constructed a curated safety dataset of 2,500 prompt-response pairs spanning benign, context-sensitive, and jailbreak categories, and fine-tuned the final six layers of LLaMA-2-7B-chat-hf into a semantic cost model reaching 92% alignment with human safety judgments.
• Benchmarked the cost model on the external XS-Test suite, scoring 0.91 to 0.96 against 0.07 to 0.32 for the Safe-RLHF baseline, demonstrating semantic grounding over keyword-triggered over-refusal.
• Demonstrated across 1,000-prompt and 1,172-pair human-preference evaluations that the trained policy flags unsafe responses 5× more efficiently than Safe-RLHF and wins 60% of head-to-head comparisons (+70 Elo) against the strongest baseline, SACPO; co-authored the resulting preprint (arXiv:2510.03520).

Advisor: Ms. Arpita Talukdar

• Raised breast-cancer classification accuracy to 97.77% on WDBC (Logistic Regression, 13 features) and 93.67% on WPBC (SVM, 18 features) by applying RFE/SFS wrapper feature selection, SMOTE class balancing, and GridSearchCV tuning across five classifiers (RF, SVM, Logistic Regression, MLP, XGBoost) under stratified 10-fold cross-validation.
• Identified the nuclear morphology features (texture, concavity, symmetry, fractal dimension) most predictive of malignancy and recurrence through a comparative analysis across model and feature-selection combinations in a dual-stage diagnosis-and-recurrence framework.